Biology subjects

Hoehndorf, R.

Publications and source records attributed to Hoehndorf, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype-phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to identifying diseaseassociated genes using computational approaches is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene-disease associations. We find that only mouse phenotypes can accurately predict human gene-disease associations. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

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bioRxiv2021

DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions

MotivationIn silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks. ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein-protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods. AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO. Contacttilman.hinnerichs@kaust.edu.sa Supplementary informationSupplementary data are available at https://github.com/THinnerichs/DTI-VOODOO.

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bioRxiv2021

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration

Combining multiple types of genomic, transcriptional, proteomic, and epigenetic datasets has the potential to reveal biological mechanisms across multiple scales, and may lead to more accurate models for clinical decision support. Developing efficient models that can derive clinical outcomes from high-dimensional data remains problematical; challenges include the integration of multiple types of omics data, inclusion of biological background knowledge, and developing machine learning models that are able to deal with this high dimensionality while having only few samples from which to derive a model. We developed DeepMOCCA, a framework for multi-omics cancer analysis. We combine different types of omics data using biological relations between genes, transcripts, and proteins, combine the multi-omics data with background knowledge in the form of protein-protein interaction networks, and use graph convolution neural networks to exploit this combination of multi-omics data and background knowledge. DeepMOCCA predicts survival time for individual patient samples for 33 cancer types and outperforms most existing survival prediction methods. Moreover, DeepMOCCA includes a graph attention mechanism which prioritizes driver genes and prognostic markers in a patient-specific manner; the attention mechanism can be used to identify drivers and prognostic markers within cohorts and individual patients. Author summaryLinking the features of tumors to a prognosis for the patient is a critical part of managing cancer. Many methods have been applied to this problem but we still lack accurate prognostic markers for many cancers. We now have more information than ever before on the state of the cancer genome, the epigenetic changes in tumors, and gene expression at both RNA and protein levels. Here, we address the question of how this data can be used to predict cancer survival and discover which tumor genes make the greatest contribution to the prognosis in individual tumor samples. We have developed a computational model, DeepMOCCA, that uses artificial neural networks underpinned by a large graph constructed from background knowledge concerning the functional interactions between genes and their products. We show that with our method, DeepMOCCA can predict cancer survival time based entirely on features of the tumor at a cellular and molecular level. The method confirms many existing genes that affect survival but for some cancers suggests new genes, either not implicated in survival before or not known to be important in that particular cancer. The ability to predict the important features in individual tumors provided by our method raises the possibility of personalized therapy based on the gene or network dominating the prognosis for that patient.

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bioRxiv2021

DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning

MotivationStructural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. ResultsWe developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. Availabilityhttps://github.com/bio-ontology-research-group/DeepSVP Contactrobert.hoehndorf@kaust.edu.sa

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bioRxiv2021

Towards Similarity-based Differential Diagnostics For Common Diseases

Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

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bioRxiv2020

Exploring binary relations for ontology extension and improved adaptation to clinical text

BackgroundThe controlled domain vocabularies provided by ontologies make them an indispensable tool for text mining. Ontologies also include semantic features in the form of taxonomy and axioms, which make annotated entities in text corpora useful for semantic analysis. Extending those semantic features may improve performance for characterisation and analytic tasks. Ontology learning techniques have previously been explored for novel ontology construction from text, though most recent approaches have focused on literature, with applications in information retrieval or human interaction tasks. We hypothesise that extension of existing ontologies using information mined from clinical narrative text may help to adapt those ontologies such that they better characterise those texts, and lead to improved classification performance. ResultsWe develop and present a framework for identifying new classes in text corpora, which can be integrated into existing ontology hierarchies. To do this, we employ the Stanford Open Information Extraction algorithm and integrate its implementation into the Komenti semantic text mining framework. To identify whether our approach leads to better characterisation of text, we present a case study, using the method to learn an adaptation to the Disease Ontology using text associated with a sample of 1,000 patient visits from the MIMIC-III critical care database. We use the adapted ontology to annotate and classify shared first diagnosis on patient visits with semantic similarity, revealing an improved performance over use of the base Disease Ontology on the set of visits the ontology was constructed from. Moreover, we show that the adapted ontology also improved performance for the same task over two additional unseen samples of 1,000 and 2,500 patient visits. ConclusionsWe report a promising new method for ontology learning and extension from text. We demonstrate that we can successfully use the method to adapt an existing ontology to a textual dataset, improving its ability to characterise the dataset, and leading to improved analytic performance, even on unseen portions of the dataset.

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bioRxiv2020

Komenti: A semantic text mining framework

SummaryKomenti is a reasoner-enabled semantic query and information extraction framework. It is the only text mining tool that enables querying inferred knowledge from biomedical ontologies. It also contains multiple novel components for vocabulary construction and context disambiguation, which can improve the power of text mining and ontology-based analysis tasks, with a view towards making full use of the semantic provision of biomedical ontologies for text characterisation and analysis. Here, we describe Komenti and its features, and present a use case wherein we automate a clinical audit, extracting medications for hypertrophic cardiomyopathy patients from text, revealing a high precision, and identifying a sub-cohort of patients with atrial fibrillation who are not anti-coagulated, and are therefore at a higher risk of stroke. Availability and ImplementationKomenti is freely available under an open source licence at http://github.com/reality/komenti. More information concerning the use-case is available in supplementary data.

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bioRxiv2020

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

BackgroundNegation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative. ResultsWe describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora. ConclusionsThe presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.

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bioRxiv2020

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

BackgroundOntologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may reveal previously hidden contradictions. ResultsWe developed a method that tests for hidden unsatisfiabilities in an ontology that arise when combined with other ontologies. For this purpose, we combine sets of ontologies and use automated reasoning to determine whether unsatisfiable classes are present. We test the mutual consistency of the OBO Foundry and the OBO ontologies and find that the combined OBO Foundry gives rise to at least 636 unsatisfiable classes, while the OBO ontologies give rise to more than 300,000 unsatisfiable classes. We design and implement a novel algorithm that can determine justifications for contradictions across extremely large and complicated ontologies, and use these justifications to semi-automatically repair ontologies by identifying the minimal set of axioms that, when removed, result in a consistent and coherent set of ontologies. We applied our algorithm to each combination of OBO ontologies that resulted in unsatisfiable classes. ConclusionsWe identified a large set of hidden unsatisfiability across a broad range of biomedical ontologies, and we find that this large set of unsatisfiable classes is the result of a relatively small amount of axiomatic disagreements. Our results show that hidden unsatisfiability is a serious problem in ontology interoperability; however, our results also provide a way towards more consistent ontologies by addressing the issues we identified.

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bioRxiv2020

Machine learning with biomedical ontologies

Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge, and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in biomedical ontologies, and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies. Key pointsO_LIOntologies provide background knowledge that can be exploited in machine learning models. C_LIO_LIOntology embeddings are structure-preserving maps from ontologies into vector spaces and provide an important method for utilizing ontologies in machine learning. Embeddings can preserve different structures in ontologies, including their graph structures, syntactic regularities, or their model-theoretic semantics. C_LIO_LIAxioms in ontologies, in particular those involving negation, can be used as constraints in optimization and machine learning to reduce the search space. C_LI

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bioRxiv2020

Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences

MotivationInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. ResultsWe developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses. Availabilityhttps://github.com/bio-ontology-research-group/DeepViral Contactrobert.hoehndorf@kaust.edu.sa

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bioRxiv2020

Self-normalizing learning on biomedical ontologies using a deep Siamese neural network

MotivationOntologies are widely used in biomedicine for the annotation and standardization of data. One of the main roles of ontologies is to provide structured background knowledge within a domain as well as a set of labels, synonyms, and definitions for the classes within a domain. The two types of information provided by ontologies have been extensively exploited in natural language processing and machine learning applications. However, they are commonly used separately, and thus it is unknown if joining the two sources of information can further benefit data analysis tasks. ResultsWe developed a novel method that applies named entity recognition and normalization methods on texts to connect the structured information in biomedical ontologies with the information contained in natural language. We apply this normalization both to literature and to the natural language information contained within ontologies themselves. The normalized ontologies and text are then used to generate embeddings, and relations between entities are predicted using a deep Siamese neural network model that takes these embeddings as input. We demonstrate that our novel embedding and prediction method using self-normalized biomedical ontologies significantly outperforms the state-of-the-art methods in embedding ontologies on two benchmark tasks: prediction of interactions between proteins and prediction of gene-disease associations. Our method also allows us to apply ontology-based annotations and axioms to the prediction of toxicological effects of chemicals where our method shows superior performance. Our method is generic and can be applied in scenarios where ontologies consisting of both structured information and natural language labels or synonyms are used. Availabilityhttps://github.com/bio-ontology-research-group/Ontology-based-normalization Contactrobert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa

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bioRxiv2020

D4: Deep Drug-drug interaction Discovery and Demystification

MotivationDrug-drug interactions (DDIs) are complex processes which may depend on many clinical and non-clinical factors. Identifying and distinguishing ways in which drugs interact remains a challenge. To minimize DDIs and to personalize treatment based on accurate stratification of patients, it is crucial that mechanisms of interaction can be identified. Most DDIs are a consequence of metabolic mechanisms of interaction, but DDIs with different mechanisms occur less frequently and are therefore more difficult to identify. ResultsWe developed a method (D4) for computationally identifying potential DDIs and determining whether they interact based on one of eleven mechanisms of interaction. D4 predicts DDIs and their mechanisms through features that are generated through a deep learning approach from phenotypic and functional knowledge about drugs, their side-effects and targets. Our findings indicate that our method is able to identify known DDIs with high accuracy and that D4 can determine mechanisms of interaction. We also identify numerous novel and potential DDIs for each mechanism of interaction and evaluate our predictions using DDIs from adverse event reporting systems. Availabilityhttps://github.com/bio-ontology-research-group/D4 Contactarnoor@kau.edu.sa and robert.hoehndorf@kaust.edu.sa

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bioRxiv2020

Predicting candidate genes from phenotypes, functions, and anatomical site of expression

MotivationOver the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patients phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. ResultsWe developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. AvailabilitySoftware and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Contactrobert.hoehndorf@kaust.edu.sa

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bioRxiv2020

DDIEM: Drug Database for Inborn Errors of Metabolism

BackgroundInborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources. ResultsWe gathered data on therapeutic strategies for 299 diseases into the Drug Database for Inborn Errors of Metabolism (DDIEM). Therapeutic approaches, including both successful and ineffective treatments, were manually classified by their mechanisms of action using a new ontology. ConclusionsWe present a manually curated, ontologically formalized knowledgebase of drugs, therapeutic procedures, and mitigated phenotypes. DDIEM is freely available through a web interface and for download at http://ddiem.phenomebrowser.net.

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bioRxiv2019

DeepPheno: Predicting single gene knockout phenotypes

MotivationPredicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. ResultsWe developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from complete loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over state of the art methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene-disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno interact with a gene that is already associated with the predicted phenotype. Availabilityhttps://github.com/bio-ontology-research-group/deeppheno Contactrobert.hoehndorf@kaust.edu.sa

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bioRxiv2019

DeepGOPlus: Improved protein function prediction from sequence

Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein-protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time.\n\nWe developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins. We evaluate the performance of DeepGOPlus on the CAFA3 dataset and significantly improve the performance of predictions of biological processes and cellular components with Fmax of 0.47 and 0.70, respectively, using only the amino acid sequence of proteins as input. DeepGOPlus can annotate around 40 protein sequences per second, thereby making fast and accurate function predictions available for a wide range of proteins.

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bioRxiv2019

Ontology-based prediction of cancer driver genes

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.

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